Data Preparation

Import Dataset

Data Preprocess

Defining Preprocess Recipe

Model Fitting

Defining Model Specifications

#> Random Forest Model Specification (classification)
#> 
#> Main Arguments:
#>   mtry = 29
#>   trees = 1000
#>   min_n = 15
#> 
#> Engine-Specific Arguments:
#>   seed = 100
#>   num.threads = parallel::detectCores()/2
#>   importance = impurity
#> 
#> Computational engine: ranger

Model Fitting

#> parsnip model object
#> 
#> Ranger result
#> 
#> Call:
#>  ranger::ranger(formula = formula, data = data, mtry = ~29, num.trees = ~1000,      min.node.size = ~15, seed = ~100, num.threads = ~parallel::detectCores()/2,      importance = ~"impurity", verbose = FALSE, probability = TRUE) 
#> 
#> Type:                             Probability estimation 
#> Number of trees:                  1000 
#> Sample size:                      380 
#> Number of independent variables:  30 
#> Mtry:                             29 
#> Target node size:                 15 
#> Variable importance mode:         impurity 
#> Splitrule:                        gini 
#> OOB prediction error (Brier s.):  0.1940542

Model Evaluation

Predict on Test Dataset

Confusion Matrix

ROC Curve

Precision-Recall Curve